store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_903179', 'seed': 903179, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[2975, 39623, 24923, 12486, 40995, 16140, 47653, 50770, 39151, 26132, 9514, 1442, 7580, 7785, 51384, 5546, 55272, 2685, 39834, 226]
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store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7948, 'nll': 1.0775544300079345}, 'chosen_samples': [37596, 58092, 16084, 25024, 13460, 25148, 20980, 8886, 22681, 19892, 45887, 5968, 49537, 591, 371, 49880, 9870, 40084, 36475, 15250, 11500, 27219, 33203, 942, 15723, 37947, 41370, 29390, 20239, 11621, 46527, 50910, 24577, 25640, 41900, 34501, 34815, 24388, 33812, 13380], 'chosen_samples_score': ['0.8974137', '0.8984657', '0.90062493', '0.90157205', '0.90293556', '0.9056035', '0.9031211', '0.9077284', '0.9058525', '0.908141', '0.96766895', '1.0102069', '0.94251037', '0.9247798', '0.93143827', '0.93373597', '0.9162293', '0.95656407', '0.9180059', '0.9169889', '0.92261684', '1.0000052', '0.9959393', '0.98076653', '0.9369831', '0.9099961', '0.9745732', '0.9571101', '0.9555155', '0.99504465', '0.9237253', '0.9724195', '0.9235893', '1.0169947', '0.93945867', '0.9428685', '0.95052993', '0.9705007', '0.9255124', '0.9640311']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8488, 'nll': 0.8334668716430664}, 'chosen_samples': [27359, 5740, 8389, 52086, 5295, 46187, 5298, 47344, 15189, 24225, 27069, 46412, 14831, 13074, 3730, 8289, 36744, 24525, 37720, 28412, 39411, 16860, 9380, 39453, 27795, 6169, 51199, 20388, 44944, 28835, 4738, 1075, 4185, 47608, 35449, 45912, 32880, 55128, 51759, 16473], 'chosen_samples_score': ['0.9321122', '0.9329557', '0.9336079', '1.0428741', '0.9845393', '0.96919394', '1.0125482', '0.9397273', '0.96793073', '0.9657245', '0.95224565', '0.9971536', '0.9517287', '1.0096474', '0.9341803', '1.1648493', '0.9558788', '0.934181', '1.0027813', '0.9883831', '0.93790865', '0.9605921', '0.93367493', '0.9751604', '0.95134395', '0.96473294', '0.9348001', '0.9569396', '0.95186496', '0.9377898', '0.95570606', '1.0487368', '0.9364134', '1.0299022', '0.98478794', '0.94931453', '0.9939272', '0.9533933', '0.95050406', '0.9524569']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8859, 'nll': 0.753139169883728}, 'chosen_samples': [38252, 50461, 44606, 12373, 56857, 57507, 36072, 414, 5548, 10275, 52729, 20171, 28771, 14047, 58250, 30493, 33752, 34765, 10412, 44255, 28898, 50825, 38054, 31246, 44202, 55311, 54481, 8813, 4340, 12964, 7932, 32276, 22675, 7004, 59747, 41635, 49202, 49760, 35275, 24827], 'chosen_samples_score': ['0.97027737', '0.9737535', '0.9976904', '0.99419945', '0.99231654', '0.9759796', '0.9773124', '0.9754737', '0.995802', '1.0067279', '0.99544775', '0.9828584', '1.009397', '0.97491336', '0.9816219', '0.9904149', '0.99426144', '1.0067332', '0.9830769', '1.0025907', '0.9865086', '1.0104065', '1.0298183', '1.0270916', '1.1324906', '1.0112894', '1.029299', '1.0700345', '1.0260601', '1.0237247', '1.1268479', '1.1006576', '1.0232557', '1.0875089', '1.030175', '1.0371594', '1.1519725', '1.0246768', '1.0214107', '1.0798459']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9135, 'nll': 0.5757267410278321}, 'chosen_samples': [12127, 30896, 2064, 42805, 11154, 40593, 49656, 21690, 44952, 38246, 6466, 39683, 32323, 31987, 10450, 48355, 49576, 4004, 27172, 9727, 32175, 37078, 36256, 3268, 20072, 55496, 3762, 9625, 42237, 28470, 59532, 42121, 10153, 15848, 20854, 22193, 40942, 42085, 18845, 15434], 'chosen_samples_score': ['0.91415906', '0.9149277', '0.91511786', '0.9166232', '0.9244292', '0.9226792', '0.9199451', '0.93997824', '0.9343674', '0.93827415', '0.9314921', '0.91982245', '0.9408472', '0.93677443', '0.9170538', '0.92643833', '0.91776216', '0.9345196', '0.9400546', '0.94186527', '0.94665855', '1.0203984', '0.96297514', '1.0019026', '0.94706553', '0.992142', '0.9441193', '1.0159266', '0.98837936', '0.94734424', '0.9636611', '1.002645', '0.99181443', '1.080142', '0.9552099', '0.95761645', '1.0225083', '0.95318955', '0.9640566', '0.9550082']})
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store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9663, 'nll': 0.32079020795822144}, 'chosen_samples': [36704, 5315, 42590, 49829, 22741, 22302, 43898, 10894, 15634, 49282, 41933, 2292, 23678, 26760, 24250, 14894, 6474, 41537, 26519, 10982, 44853, 28368, 48006, 52694, 44732, 22481, 52975, 14201, 20641, 56662, 39519, 8883, 18324, 43745, 41816, 11643, 10044, 2202, 49607, 26072], 'chosen_samples_score': ['0.9330924', '0.93746996', '0.93979424', '0.93974227', '0.9422344', '0.95375675', '0.9445655', '0.9442179', '0.9432722', '0.94656545', '0.95426375', '1.0133089', '1.1609362', '0.97732925', '1.0480509', '1.0498488', '1.0252135', '0.9945471', '0.97527885', '1.0088835', '0.99078953', '1.0755188', '1.0453585', '0.9602019', '1.0196195', '0.965723', '0.9703495', '1.039484', '1.0203407', '0.9900239', '0.9692924', '1.066165', '0.95830315', '0.977068', '0.9969097', '0.9593721', '0.95653003', '0.9795869', '1.0761731', '0.9574734']})
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